Local Matching Feature Extraction.    Our feature extractor F-Net is based on PairNet [1]}. It is a lightweight Feature Pyramid Network (FPN) [2]} on top of first fourteen layers of MnasNet [3]}. Specifically, the reference input image \(I_0 \in \mathbb {R} ^{H \times W}\)  is spatially scaled down until 1/32 scale, and recovered up to 1/2 scale, resulting in multi-scale features \(\lbrace f_{0,s} \in \mathbb {R} ^{\frac{H}{s} \times \frac{W}{s} \times F_0} \rbrace \)  (\(s\) =2,4,8,16 and \(F_0\) =32 for feature channels). Unlike the PairNet, where \(f_{0,2}\)  is used to construct the cost volume, and other features are used in the skip connections to a series of decoders for depth regression, we add an extra fusion layer \(\mathcal {G}\)  to aggregate them into a matching feature \(f_0\)  at 1/4 scale, as
\(f_0 = \mathcal {G}(\langle f_{0,2}\downarrow _2, f_{0,4}, f_{0,8}\uparrow _2, f_{0,16}\uparrow _4 \rangle )\) 
